
"""simple docstring"""


import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				List[Any]							,   UpperCAmelCase_			:				VQModel							,   UpperCAmelCase_			:				UNetaDModel							,   UpperCAmelCase_			:				DDIMScheduler      )->	str:







              """simple docstring"""
              super().__init__()
              self.register_modules(vqvae=UpperCAmelCase_							,   unet=UpperCAmelCase_							,   scheduler=UpperCAmelCase_      )






       @torch.no_grad()
       def __call__(	self			:				Optional[int]							,   UpperCAmelCase_			:				int = 1							,   UpperCAmelCase_			:				Optional[Union[torch.Generator, List[torch.Generator]]] = None							,   UpperCAmelCase_			:				float = 0.0							,   UpperCAmelCase_			:				int = 50							,   UpperCAmelCase_			:				Optional[str] = "pil"							,   UpperCAmelCase_			:				bool = True							,   **UpperCAmelCase_			:				List[str]							,   )->	Union[Tuple, ImagePipelineOutput]:







              """simple docstring"""
              UpperCamelCase       			=		randn_tensor(
                  (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,   generator=UpperCAmelCase_							,   )
              UpperCamelCase       			=		latents.to(self.device      )

              # scale the initial noise by the standard deviation required by the scheduler
              UpperCamelCase       			=		latents * self.scheduler.init_noise_sigma

              self.scheduler.set_timesteps(UpperCAmelCase_      )

              # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
              UpperCamelCase       			=		"eta" in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )

              UpperCamelCase       			=		{}
              if accepts_eta:
                     UpperCamelCase       			=		eta

              for t in self.progress_bar(self.scheduler.timesteps      ):
                     UpperCamelCase       			=		self.scheduler.scale_model_input(UpperCAmelCase_							,   UpperCAmelCase_      )
                     # predict the noise residual
                     UpperCamelCase       			=		self.unet(UpperCAmelCase_							,   UpperCAmelCase_      ).sample
                     # compute the previous noisy sample x_t -> x_t-1
                     UpperCamelCase       			=		self.scheduler.step(UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   **UpperCAmelCase_      ).prev_sample

              # decode the image latents with the VAE
              UpperCamelCase       			=		self.vqvae.decode(UpperCAmelCase_      ).sample

              UpperCamelCase       			=		(image / 2 + 0.5).clamp(0							,   1      )
              UpperCamelCase       			=		image.cpu().permute(0							,   2							,   3							,   1      ).numpy()
              if output_type == "pil":
                     UpperCamelCase       			=		self.numpy_to_pil(UpperCAmelCase_      )

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=UpperCAmelCase_      )







"""simple docstring"""


SCREAMING_SNAKE_CASE			  =  {}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				int:







       """simple docstring"""

       # if we are absent twice, or late 3 consecutive days,
       # no further prize strings are possible
       if late == 3 or absent == 2:
              return 0

       # if we have no days left, and have not failed any other rules,
       # we have a prize string
       if days == 0:
              return 1

       # No easy solution, so now we need to do the recursive calculation

       # First, check if the combination is already in the cache, and
       # if yes, return the stored value from there since we already
       # know the number of possible prize strings from this point on
       UpperCamelCase       			=		(days, absent, late)
       if key in cache:
              return cache[key]

       # now we calculate the three possible ways that can unfold from
       # this point on, depending on our attendance today

       # 1) if we are late (but not absent), the "absent" counter stays as
       # it is, but the "late" counter increases by one
       UpperCamelCase       			=		_calculate(days - 1		,   UpperCAmelCase_		,   late + 1					)

       # 2) if we are absent, the "absent" counter increases by 1, and the
       # "late" counter resets to 0
       UpperCamelCase       			=		_calculate(days - 1		,   absent + 1		,   0					)

       # 3) if we are on time, this resets the "late" counter and keeps the
       # absent counter
       UpperCamelCase       			=		_calculate(days - 1		,   UpperCAmelCase_		,   0					)

       UpperCamelCase       			=		state_late + state_absent + state_ontime

       UpperCamelCase       			=		prizestrings
       return prizestrings



def 						lowerCamelCase__      (				UpperCAmelCase_ = 30					)->				int:







       """simple docstring"""
       return _calculate(UpperCAmelCase_		,   absent=0		,   late=0					)


if __name__ == "__main__":
      print(solution())





